In this study, unsupervised MFCC based GMM classifiers are employed for pronunciation modeling.

english dialects differ in many ways other than pronunciation like Word Selection and Grammar, which cannot be modeled using frame based GMM acoustic information.


These are both serious challenges for data-driven methods and could be addressed with the integration of linguistic resources.

there is more work to be done on data driven methods.


Similar to standard boosting, this bound shows that the training score can be improved exponentially in the number of iterations.

we found that the conditions for which this bound is applicable is rarely satisfied in our experiments.


These models typically view a sentence either as a bag of words (Foltz et al., 1998) or as a bag of entities associated with various syntactic roles .

a mention of an entity contains more information than just its head and syntactic role.


Their method of combination is quite different from ours; they use the system's judgements to define the "entities" whose repetitions the system measures 7 .

we do not attempt to use any proposed coreference links; as  point out, these links are often erroneous because the disorded input text is so dissimilar to the training data.


The b 3 scorer (Amit and Baldwin, 1998) was proposed to overcome several shortcomings of the MUC scorer.

coreference resolution is a clustering task, and many cluster scorers already exist.


When using the MUC scorer, the ILP system always did worse than the D&B-STYLE baseline.

this is precisely because the transitivity constraints tend to yield smaller clusters (which increase precision while decreasing recall).


Proper name transliteration is primarily handled by TRANSEX.

an OOV with a different spelling of an INV name can be handled by SPELLEX.


Our average baseline BLEU score went up from 42.60 to 45.00.

using the ALL combination, we still increase the scaled-up system's score to an average BLEU of 45.28 (0.61% relative).


The best improvement is The comparison of the SVM results to the results of previous (Section 2) shows that our system achieves relatively high accuracy.

most previous systems researched ambiguous abbreviations in the English language, as well as different abbreviations and texts.


However, the purpose of this paper is to create a framework for accounting for cost in AL algorithms.

to the model presented by Ngai and Yarowsky (2000), which predicts monetary cost given time spent, this model estimates time spent from characteristics of a sentence.


Efficiency is maintained because such arbitrary disjunction is not needed to encode the most common forms of uncertainty, and thus the number of MDP states in the set can be kept small without losing accuracy.

allowing multiple MDP states provides the representational mechanism necessary to incorporate multiple speech recognition hypotheses into the belief state representation.


Lexical ambiguity resolution is an important research problem for the fields of information retrieval and machine translation (Sanderson, 2000;Chan et al., 2007).

making fine-grained sense distinctions for words with multiple closelyrelated meanings is a subjective task (Jorgenson, 1990;Palmer et al., 2005), which makes it difficult and error-prone.


Currently, the only indicators under consideration are "and" and "or".

more patterns can be included in the future.


The shared components in these groups are radicals of the characters, so we can find the characters of the same group in the same section in a Chinese dictionary.

information about radicals as they are defined by the lexicographers is not sufficient.


Certainly, more sophisticated personalization models and user clustering methods could be devised.

as we show next, even the simple models described above prove surprisingly effective.


Also note that baseline ASP classifier is not able to achieve higher accuracy even for users with large amount of past history.

the ASP Pers+Text classifier, trained only on the past question(s) of each user, achieves surprisingly good accuracy -often significantly outperforming the ASP and ASP Text classifiers.


While self-training has worked in several domains, the early results on self-training for parsing were negative (Steedman et al., 2003;Charniak, 1997).

more recent results have shown that it can indeed improve parser performance (Bacchiani et al., 2006;McClosky et al., 2006a;McClosky et al., 2006b).


The Brown corpus has an out-of-vocabulary rate of approximately 6% when given WSJ training as the lexicon.

the out-of-vocabulary rate of biomedical abstracts given the same lexicon is significantly higher at about 25% (Lease and Charniak, 2005).


several very good current parsers were not available when this paper was written (e.g., the Berkeley Parser (Petrov et al., 2006)).


Clegg and Shepherd (2005) do not provide separate precision and recall numbers.

we can see that the reranker) modified to use an in-domain tagger.


At 80.4, it is clearly the worst of the lot.

it is already better than the 80.2% best previous result for biomedical data.


In this paper, similarly to our previous approach, we design an SVM-based answer extractor, that selects the correct answers from those provided by a basic QA system by applying tree kernel technology.

we also provide: (i) a new kernel to process PASs based on the partial tree kernel algorithm (PAS-PTK), which is highly more efficient and more accurate than the SSTK and (ii) a new kernel called Part of Speech sequence kernel (POSSK), which proves very accurate to represent shallow syntactic information in the learning algorithm.


MorphAll is the hardest of the three morphological tagging tasks, subsuming MorphPart and MorphPOS, and DiacFull is the hardest lexical task, subsuming DiacPart, which in turn subsumes LexChoice.

morphAll and DiacFull are (in general) orthogonal, since morphAll has no lexemic component, while DiacFull does.


When designing a dialog manager for a spoken dialog system, we would ideally like to try different dialog management strategies on the actual user population that will be using the system, and select the one that works best.

users are typically unwilling to endure this kind of experimentation.


The usual method of building a user model is to estimate it from transcribed corpora of human-computer dialogs.

manually transcribing dialogs is expensive, and consequently these corpora are usually small and sparse.


All of them require a corpus whose NEs are annotated properly as training data.

it is difficult to obtain an enough corpus in the real world, because there are increasing the number of NEs like personal names and company names.


These are then used as input to the summarisation process.

modelling user needs is a difficult task.


This is similar to the elaborative goal of our summary in the sense that one could answer the question: "What else can I say about topic X (that hasn't already been mentioned in the reading context)".

whereas DUC focused on unlinked news wire text, we explore a different genre of text.


It is difficult to beat the first-5 baseline, which attains the best recall of 0.52 and a precision of 0.2, with all other strategies falling behind.

we believe that this may be due to the presence of some types of Wikipedia articles that are narrow in scope and centered on specific events.


The important idea in Kneser-Ney is to let the probability of a back-off n-gram be proportional to the number of unique words that precede it.

we do not need to use the absolute discount form for the estimates.


The hierarchical phrase-based model (Chiang, 2005) used hierarchical phrase pairs to strengthen the generalization ability of phrases and allow long distance reorderings.

the huge grammar table greatly increases computational complexity.


For this task, the BLEU score of the baseline is 30.45.

for partial matching method with  α=0.5 3 , the BLEU score is 30.96, achieving an absolute improvement of 0.51.


The improvement on large-scale task is less than that on small-scale task since larger corpus relieves data sparseness.

the partial matching approach can also improve translation quality by using long phrases.


The results for the probabilistic models for infering lexical semantic roles are shown in Table 3, where the term naive means that no WordNet features were included in the training of the models, but only simple features like noun rel for nouns.

when mode is complete, WordNet hypernyms up to the 5th level in the hierarchy were used.


Which type is more useful is likely to depend on the kind of application and information needs of the user, but this is essentially still an open question.


In the news article domain, ROUGE scores have been shown to be generally highly correlated with human evaluation in content match (Lin, 2004).

there are many differences between written texts (e.g., news wire) and spoken documents, especially in the meeting domain, for example, the presence of disfluencies and multiple speakers, and the lack of structure in spontaneous utterances.


Inspired by automatic speech recognition (ASR) evaluation, (Hori et al., 2003) proposed the summarization accuracy metric (SumACCY) based on a word network created by merging manual summaries.

(Zhu and Penn, 2005) found a statistically significant difference between the ASR-inspired metrics and those taken from text summarization (e.g., RU, ROUGE) on a subset of the Switchboard data.


There have been efforts on the study of the impact of disfluencies on summarization techniques (Liu et al., 2007;Zhu and Penn, 2006) and human readability (Jones et al., 2003).

it is not clear whether disfluencies impact automatic evaluation of extractive meeting summarization.


Our analysis suggests that simple word frequency computations of these clusters and the documents alone can produce reasonable summaries.

the human selecting the relevant documents may have already influenced the way summaries can automatically be generated.


Both classifiers misclassify approximately 45% of the Noisy00 sentences.

the sentences misclassified by the E2prob classifier are those that are handled well by the E0 parser, and this is reflected in the parsing results for Noisy00.


Most previous researches on speakers' intentions have been focused on intention identification techniques.

intention prediction techniques have been not studied enough although there are many practical needs, as shown in Figure  1.


Reithinger showed that his model can reduce the searching complexity of an ASR to 19~60%.

his model did not achieve good performances because the input features were not rich enough to predict next speech acts.


This technique reduces the number of support vectors in each classifier (because each classifier was trained on only a portion of the data).

it relies on human intuition on the way the data should be split, and usually results in a degradation in performance relative to a single classifier trained on all the data points.


It is because of common features that the PKI reverse indexing method does not yield great improvements: if at least one of the features of the current instance is active in a support vector, this vector is taken into account in the sum calculation, and the common features are active in many support vectors.

the long tail of rare features is the reason the Kernel Expansion methods requires so much space: every rare feature adds many possible feature pairs.


Our approach is similar to the PKE approach (Kudo and Matsumoto, 2003), which used a basket mining approach to prune many features from the expansion.

we use a simpler approach to choose which features to include in the expansion, and we also compensate for the feature we did not include by the PKI method.


Similarly, the noun phrases the pattern of pigments and the bunch of leaves typically result in identical dependency parses.

the word pattern is considered the governor of pigments; whereas, conversely the word leaves is treated as the governor of bunch because it carries more semantics.


While this algorithm works well with underspecified semantic representations in semantics, it is too slow for the larger discourse graphs, as we will see in Section 5.

we will now optimise it for the special case of constrained chains.


In fact, WordNet, with its broad coverage and easy accessibility, has become the resource of choice for WSD.

some have questioned whether WordNet's fine-grained sense distinctions are appropriate for the task (Ide & Wilks, 2007;Palmer et al., 2007).


The geometric mean of the frequencies of compound parts and the probability estimated from the language model usually attain a high recall, given they are based on unigram features which are easy to collect, but they have some weaknesses, as mentioned above.

while Mutual Information is a much more precise metric, it is less likely to have evidence about every single possible pair of compound parts from a corpus, so it suffers from low recall.


Thus it can not capture the unbalanced information.

metalearning is able to learn the unbalance automatically through training the meta-classifier using the development data.


keys normal − keys with prediction keys normal × 100% A word prediction system that offers higher savings will benefit a user more in practice.

the equation for keystroke savings has two major deficiencies.


In our case, eliminating the top 1% of terms reduces the number of document pairs by several orders of magnitude.

the impact of this technique on effectiveness (e.g., in a query-by-example experiment) has not yet been characterized.


Traditional machine learning relies on the availability of a large amount of data to train a model, which is then applied to test data in the same feature space.

labeled data are often scarce and expensive to obtain.


In addition, most of them are designed for supervised learning.

in practice, we often face the problem where the labeled data are scarce in their own feature space, whereas there may be a large amount of labeled heterogeneous data in another feature space.


In principle, it should be possible to use existing sense-annotated data to explore this question: almost all sense annotation efforts have allowed annotators to assign multiple senses to a single occurrence, and the distribution of these sense labels should indicate whether annotators viewed the senses as disjoint or not.

the percentage of markables that received multiple sense labels in existing corpora is small, and it varies massively between corpora: In the SemCor corpus (Landes et al., 1998), only 0.3% of all markables received multiple sense labels.


In addition, there is a strong correlation between WSsim and Usim, which indicates that the potential bias introduced by the use of dictionary senses in WSsim is not too prominent.

we note that WSsim only contained a small portion of 3 lemmas (30 sentences and 135 SPAIRs) in common with Usim, so more annotation is needed to be certain of this relationship.


Some previous studies have employed this idea to remedy the data sparseness problem in the training data (Gildea and Jurafsky, 2002).

we cannot apply this approach when multiple roles in Y f are contained in the same class.


Swier and Stevenson (2004) and Swier and Stevenson (2005) presented the first model that does not use an SRL annotated corpus.

they utilize the extensive verb lexicon Verb-Net, which lists the possible argument structures allowable for each verb, and supervised syntactic tools.


Notable examples include (Manning, 1993;Briscoe and Carroll, 1997;Korhonen, 2002) who all used statistical hypothesis testing to filter a parser's output for arguments, with the goal of compiling verb subcategorization lexicons.

these works differ from ours as they attempt to characterize the behavior of a verb type, by collecting statistics from various instances of that verb, and not to determine which are the arguments of specific verb instances.


Next, during labeling, the precise verb-specific roles for each word are determined.

to the approach in (Punyakanok et al., 2008), which tags constituents directly, we tag headwords and then associate them with a constituent, as in a previous CCG-based approach (Gildea and Hockenmaier, 2003).


In the sentence the company stopped using asbestos in 1956 (figure 7), the correct Arg1 of stopped is using asbestos.

because in 1956 is erroneously modifying the verb using rather than the verb stopped in the treebank parse, the system trusts the syntactic analysis and places Arg1 of stopped on using asbestos in 1956.


It is important to acquire additional labeled data for the target grammar parsing through exploitation of existing source treebanks since there is often a shortage of labeled data.

to our knowledge, there is no previous study on this issue.


A possible solution is to simply concatenate the two treebanks as training data.

this method may lead to a problem that if the size of C P S is significantly less than that of converted C DS , converted C DS may weaken the effect C P S might have.


The reason is straightforward, syntactic structure is too complicated to be properly translated and the cost of translation cannot be afforded in many cases.

we empirically find this difficulty may be dramatically alleviated as dependencies rather than phrases are used for syntactic structure representation.


Among of existing works that we are aware of, we regard that the most similar one to ours is (Zeman and Resnik, 2008), who adapted a parser to a new language that is much poorer in linguistic resources than the source language.

there are two main differences between their work and ours.


In an early version of our approach, the former was implemented.

it is proven to be quite inefficient in computation.


Table 5 shows the results achieved by other researchers and ours (UAS with p), which indicates that our parser outperforms any other ones 4 .

our results is only slightly better than that of (Chen et al., 2008) as only sentences whose lengths are less than 40 are considered.


Sister-head dependencies are useful in this case because of the flat structure of NEGRA's trees.

to the deeper approaches to parsing described above, topological field parsing identifies the major sections of a sentence in relation to the clausal main verb and subordinating heads, when present.


(Kübler et al., 2006;Dubey and Keller, 2003) in the case of German).

topological fields explain a higher level of structure pertaining to clause-level word order, and we hypothesize that lexicalization is unlikely to be helpful.


In another strand of work, syntactic annotations are assumed on both sides of the parallel data, and a model is trained to exploit the parallel data at test time as well (Smith and Smith, 2004;Burkett and Klein, 2008).

to this work, our goal is to explore the benefits of multilingual grammar induction in a fully unsupervised setting.


We could rewrite this by explicitly integrating over the yield, context, coupling, Giza-score parameters as well as the alignment trees.

since maximizing this integral directly would be intractable, we resort to standard Markov chain sampling techniques.


These marginals can be efficiently pre-computed and form the "inside" table of the famous Inside-Outside algorithm.

in our setting, trees come in pairs, and their joint probability crucially depends on their alignment.


For Korean we found that the baseline performed well using these values.

on our English and Chinese data, we found that somewhat higher smoothing values worked best, so we utilized values of 20 and 80 for constituent and distituent smoothing counts, respectively.


the fields f , the words w are generated independently: 4 where r(k) and f (k) are the record and field responsible for generating word w k , as determined by the segmentation c. The word choice model p w (w | t, v) specifies a distribution over words given the field type t and field value v. This distribution is a mixture of a global backoff distribution over words and a field-specific distribution which depends on the field type t. Although we designed our word choice model to be relatively general, it is undoubtedly influenced by the three domains.

we can readily extend or replace it with an alternative if desired; this modularity is one principal benefit of probabilistic modeling.


Inspection of the errors revealed the following problem: The alignment task requires us to sometimes align a sentence to multiple redundant records (e.g., play and score) referenced by the same part of the text.

our model generates each part of text from only one record, and thus it can only allow an alignment to one record.


This maximizes the probability of word segmentation w given a string s : This approach often implicitly includes heuristic criteria proposed so far 1 , while having a clear statistical semantics to find the most probable word segmentation that will maximize the probability of the data, here the strings.

they are still naïve with respect to word spellings, and the inference is very slow owing to inefficient Gibbs sampling.


The inference of this model interleaves adding and removing a customer to optimize t hw , d, and θ using MCMC.

in our case "words" are not known a priori: the next section describes how to accomplish this by constructing a nested HPYLM of words and characters, with the associated inference algorithm.


To avoid dependency on ngram order n, we actually used the ∞-gram language model (Mochihashi and Sumita, 2007), a variable order HPYLM, for characters.

for generality we hereafter state that we used the HPYLM.


This quantity is random since the observation string itself is.

we note that the distribution of O n is unaffected if one relabels the words in Ω n .


Correct stress placement is important in textto-speech systems because it affects the accuracy of human word recognition (Tagliapietra and Tabossi, 2005;Arciuli and Cupples, 2006).

the issue has often been ignored in previous letter-to-phoneme (L2P) systems.


In many languages, only two levels of stress are distinguished: stressed and unstressed.

some languages exhibit more than two levels of stress.


Performance is 1-3% lower on letters than on phonemes.

the performance of ORACLESYL drops much less on letters.


Following the above approaches, we can expand the input or output symbols of our L2P system to include stress.

since both decision tree systems and our L2P predictor utilize only local context, they may produce invalid global output.


Numerous studies have demonstrated that active learners can make more efficient use of unlabelled data than do passive learners (Abe and Mamitsuka, 1998;Miller et al., 2004;Culotta and McCallum, 2005).

relatively few researchers have applied active learning techniques to the L2P domain.


5 The idea of lowering the specificity of letter class questions as the context length increases is due to Kienappel and Kneser (2001), and is intended to avoid overfitting.

their configuration differs from ours in that they use longer context lengths (4 for German and 5 for English) and ask letter class questions at every position.


Oh and Choi (2002) define classes of phonemes and assign various distances between phonemes of different classes.

we make use of phonological descriptors to define the similarity between phonemes in this paper.


the experiments with the tree structure were very successful.


By doing so, we can guarantee to use at least all the top n best parse trees in the forest.

please note that even after pruning there is still exponential number of additional trees embedded in the forest because of the sharing structure of forest.


Although a forest can cover much more phrases than a single tree does, there are still many non-syntactic phrases that cannot be captured by a forest due to structure divergence issue.

tree sequence is a good solution to non-syntactic translation equivalence modeling.


3) In Table 3, the fully-lexicalized rules are the major part (around 60%), followed by the partially-lexicalized (around 35%) and unlexicalized (around 15%).

in Table 2, partially-lexicalized rules extracted from training corpus are the major part (more than 70%).


Another option for the contribution of OOV phrases is to take log of expected probability ratio (LEPR): It is not difficult to prove that there is no difference between Model 1 and Model 2 when ELPR scoring is used for sentence selection.

the situation is different for LEPR scoring: the two models produce different sentence rankings in this case.


(Callison-Burch and Osborne, 2003b;Callison-Burch and Osborne, 2003a) provide a co-training approach to MT, where one language pair creates data for another language pair.

our co-training approach uses consensus translations and our setting for active learning is very different from their semi-supervised setting.


As properties in infobox are not complete sentences and do not present relevant arguments, it is inappropriate to concatenate them as a summary.

they are good indicators for summary generation.


This strategy is commonly used in multi-document summarization (Barzilay et al., 1999;Goldstein et al., 2000;Radev et al., 2000), where the combination step eliminates the redundancy across selected excerpts.

separating the two steps may not be optimal for this task -the balance between coverage and redundancy is harder to achieve when a multi-paragraph summary is generated.


The NLG process (Reiter and Dale, 2000) is often viewed as a pipeline consisting of content planning (selecting and structuring the story's content), microplanning (sentence ag-gregation, generation of referring expressions, lexical choice), and surface realization (agreement, verb-subject ordering).

story generation systems typically operate in two phases: (a) creating a plot for the story and (b) transforming it into text (often by means of template-based NLG).


While debating, participants often refer to and acknowledge the viewpoints of the opposing side.


An aspect may be associated with both of the debate topics, but not, by itself, be discriminative between stances toward the topics.

opinions toward that aspect might discriminate between them.


This results in Recall and Accuracy being the same.

all of the systems do not classify a post if the post does not contain the information it needs.


Because the iPhone and the Blackberry are both phones, the word "phone" does not have any distinguishing power in debates.

the PMI measure of "e-mail" suggests that it is not closely related to the debate topics, though it is, in fact, a desirable feature for smart phone users, even more so with Blackberry users.


In the second sentence, the lexicon does hit the word "worth," and, using syntactic rules, we can determine it is negated.

the opinion-target pairing system only tells us that the opinion is tied to the "it."


Theirs is a supervised bag-of-words system using unigrams, bigrams, and trigrams as features.

our approach is unsupervised, and exploits different types of information.


Several researchers have recognized the important role discourse plays in opinion analysis (Polanyi and Zaenen, 2005;Snyder and Barzilay, 2007;Somasundaran et al., 2008;Asher et al., 2008;Sadamitsu et al., 2008).

previous work did not account for concessions in determining whether an opinion supports one side or the other.


The lack of Chinese sentiment corpora limits the research progress on Chinese sentiment classification.

there are many freely available English sentiment corpora on the Web.


Actually, TSVM(ENCN2) is similar to CoTrain because CoTrain also combines the results of two classifiers in the same way.

the co-training approach can train two more effective classifiers, and the accuracy values of the component English and Chinese classifiers are 0.775 and 0.790, respectively, which are higher than the corresponding TSVM classifiers.


Though, their techniques for proving algorithm convergence and correctness can be readily adapted for our models, (Li et al., 2008) do not incorporate dual supervision as we do.

while (Sindhwani et al., 2008) do incorporate dual supervision in a non-linear kernelbased setting, they do not enforce non-negativity or orthogonality -aspects of matrix factorization models that have shown benefits in prior empirical studies, see e.g., (Ding et al., 2006).


Ideally, we would like to evaluate a random sample of the more than 1,000 languages represented in PANDICTIONARY.

5 a high-quality evaluation of translation between two languages requires a person who is fluent in both languages.


Recent studies Kozareva et al., 2008) show that if the size of a corpus, such as the Web, is nearly unlimited, a pattern has a higher chance to explicitly appear in the corpus.

corpus size is often not that large; hence the problem still exists.


The annotated PropBank corpus, and therefore implicitly its role labels inventory, has been largely adopted in NLP because of its exhaustiveness and because it is coupled with syntactic annotation, properties that make it very attractive for the automatic learning of these roles and their further applications to NLP tasks.

the labelling choices made by PropBank have recently come under scrutiny (Zapirain et al., 2008;.


Arguments receiving labels A0-A5 or AA do not express consistent semantic roles and are specific to a verb, while arguments receiving an AM-X label are supposed to be adjuncts and the respective roles they express are consistent across all verbs.

among argument labels, A0 and A1 are assigned attempting to capture Proto-Agent and Proto-Patient properties (Dowty, 1991).


For example, Agent and Instrumental Cause are often subjects (of verbs selecting animate and inanimate subjects respectively); Patients/Themes can be direct objects of transitive verbs and subjects of change of state verbs; Goal and Beneficiary can be passivised and undergo the dative alternation; Instrument and Comitative are expressed by the same preposition in many languages (see Levin and Rappaport Hovav (2005).)

most annotation schemes in NLP and linguistics assume that semantic role labels are atomic.


The resulting scores are cheap and objective.


This matches well with our intuitions: We see some noise-induced degradation for the entailment features, but not much.

surface-based features are better at detecting bad translations than at discriminating among good ones.


These automatic evaluation metrics allow developers to optimize their systems without the need for expensive human assessments for each of their possible system configurations.

estimating the system output quality according to its similarity to human references is not a trivial task.


In order to tackle language variability in the context of Machine Translation, a considerable effort has also been made to include deeper linguistic information in automatic evaluation metrics, both syntactic and semantic (see Section 2 for details).

the most commonly used metrics are still based on n-gram matching.


Therefore, we need additional meta-evaluation criteria in order to clarify the behavior of linguistic metrics as compared to n-gram based metrics.


This result supports the intuition in (Banerjee and Lavie, 2005) that correlation at segment level is necessary to ensure the reliability of metrics in different situations.

the correlation values of metrics at segment level have also drawbacks related to their interpretability.


In general, the linguistic metrics do not improve the ability to capture wrong translations (horizontal axis in the figure).

again, the combining metric ULC achieves the same reliability as the best n-gram based metric.


To better leverage syntactic constraint yet still allow non-syntactic translations, Chiang (2005) introduces a count for each hypothesis and accumulates it whenever the hypothesis exactly matches syntactic boundaries on the source side.

and Cherry (2008) accumulate a count whenever hypotheses violate constituent boundaries.


Similar to previous methods, our SDB model is integrated into the decoder's log-linear model as a feature so that we can inherit the idea of soft constraints.

to the constituent matching/violation counting (CMVC) (Chiang, 2005;Cherry, 2008), our SDB model has 2 Here we expand the definition of phrase to include both syntactic and non-syntactic phrases.


Interestingly, hierarchical phrase-based models provide this bene t without making any linguistic commitments beyond the structure of the model.

the system's lack of linguistic commitment is also responsible for one of its great-est drawbacks.


1 into English using the following set of rules: To correctly translate the sentence, a hierarchical phrase-based system needs to model the subject noun phrase, object noun phrase and copula constructions; these are captured by rules X a , X d and X b respectively, so this set of rules represents a hierarchical phrase-based system that can be used to correctly translate the Chinese sentence.

note that the Chinese word order is correctly preserved in the subject (X a ) as well as copula constructions (X b ), and correctly inverted in the object construc- although it can generate the correct translation in Fig Figure 2: The derivation that leads to the correct translation   The computation of the dominance relationship using this alignment information will be discussed in detail in the next section.


Successful phrase-based systems typically employ language models of order higher than two.

our models so far have the following important "Markovian" property: the cost of a path is additive relative to the costs of transitions.


While this problem was manageable for the artificial monolingual word re-ordering (which had only one possible translation for each source word), it becomes unwieldy for the real translation experiments, which is why in this paper we only considered bigram LMs for these experiments.

we know how to handle this problem in principle, and we now describe a method that we plan to experiment with in the future.


The Minkowski-Weyl theorem (Rockafellar, 1970) ensures that Z(x) has a representation of the form Z(x) = {z ∈ R |A| | Az ≤ b}, for some p-by-|A| matrix A and some vector b in R p .

it is not easy to obtain a compact representation (where p grows polynomially with the number of words n).


Note that conditions 1-2-3 are equivalent to 1-2-3 , in the sense that both define the same set Y(x).

as we will see, the latter set of conditions is more convenient.


Thus, transition-based parsers normally run in linear or quadratic time, using greedy deterministic search or fixed-width beam search Attardi, 2006;Johansson and Nugues, 2007;Titov and Henderson, 2007), and graph-based models support exact inference in at most cubic time, which is efficient enough to make global discriminative training practically feasible (McDonald et al., 2005a;McDonald et al., 2005b).

one problem that still has not found a satisfactory solution in data-driven dependency parsing is the treatment of discontinuous syntactic constructions, usually modeled by non-projective dependency trees, as illustrated in Figure 1.


Additionally prototype-driven grammar induction needs to be used in conjunction with other unsupervised methods (distributional similarity and CCM (Klein and Manning, 2004)) to attain reasonable accuracy, and is only evaluated on length 10 or less sentences with no lexical information.

gE uses only the provided constraints and unparsed sentences, and is used to train a feature-rich discriminative model.


The development and tuning of the above methods constitute the encoding of prior domain knowledge about the desired syntactic structure.

our framework provides a straightforward and explicit method for incorporating prior knowledge.


If there are constraint functions G for all model feature functions F j , and the target expectations G are estimated from labeled data, then the globally optimal parameter setting under the GE objective function is equivalent to the maximum likelihood solution.

gE does not require such a one-to-one correspondence between constraint functions and model feature functions.


Because these features consider multiple edges, including them in the CRF model would make exact inference intractable (McDonald and Satta, 2007).

the CRF may consider the distance between head and child, whereas DMV does not model distance.


We can see that the name chunk usually has the largest mutual information.

the name chunk always needs to be transliterated, and transliteration is often more difficult than translation by lexicon.


We exploit this wide variation with bagging, sampling from automatically extracted seeds to reduce semantic drift.

semantic drift still occurs in later iterations.


This second criterion aims to increase recall.

the selected instances are highly likely to introduce drift.


A drift(t, n, m) of 0.2 corresponds to a 20% difference in average similarity between L 1...n and L (N −m)...N for term t. Drift can be used as a post-processing step to filter terms that are a possible consequence of drift.

our main proposal is to incorporate the drift measure directly within the WMEB bootstrapping algorithm, to detect and then prevent drift occuring.


These types of relations have mostly been identified in isolation by event pairwise comparison.

this approach neglects logical constraints between temporal relations of different types that we believe to be helpful.


3 Only for Task B we were unable to reach the performance of a rule-based entry to the challenge.

we do perform better than all pure machine 1 It is clearly possible to incorporate weighted constraints into ILPs, but how to learn the corresponding weights is not obvious.


The intuition behind the previous formula can also be captured using a local classifier.

6 Markov Logic also allows us to say more: beforeDCT (e 1 ) ∧ ¬beforeDCT (e 2 ) ⇒ before (e 1 , e 2 ) (2) In this case, we made a statement about more global properties of a temporal ordering that cannot be captured with local classifiers.


One way is to pick a task and then choose formulae that increase the accuracy for this task on DEV.

our primary goal is to improve the performance of all the tasks together.


We have presented a profile-based Cross Document Coreference (CDC) approach based on a novel fuzzy relational clustering algorithm KARC.

to traditional hard clustering methods, KARC produces fuzzy sets of identities which better reflect the intrinsic uncertainty of the CDC problem.


Although MT may be used in solving this task, it is only used by the algorithms -the final evaluation is done in the source language.

in many real-life situations, such as global business, international tourism, or intelligence work, users may not be able to read the source language.


Since this annotation task was a 10-way selection, with multiple selections possible, there were some disagreements.

if categorized broadly into 5W System errors only, MT errors only, and both 5W System and MT errors, then the annotators had a substantial level of agreement (κ=0.75 for error type, on sentences where both annotators indicated an error).


English-LF also had more Partial answers on the What question: 66% Correct and 12% Partial, versus 75% Correct and 1% Partial for English-function.

englishfunction was more likely to return answers that contained incorrect extra information, such as another 5W or a second predicate.


The results show that, on average, ASIA performs better.

we should emphasize that for the three classes: movie, person, and video game, ASIA did not initially converge to the correct instance list given the most natural concept name.


Indeed, the commonly used hyponymy, synonymy and some cases of the meronymy relations are special cases of lexical reference.

lexical reference is a broader relation.


However this is a rather loose notion, which only indicates that terms are semantically "related" and are likely to co-occur with each other.

lexical reference is a special case of lexical association, which specifies concretely that a reference to the meaning of one term may be inferred from the other.


It is worth noting that in our sample 57% of All-N errors, 62%  These three rows replace the last row of Table 2 Places were extracted by the All-N bottom method and thus may be identified as less reliable.

this split was not observed to improve performance in the application oriented evaluations of Section 6.


Following our error analysis, future research is needed for addressing each specific type of error.

during the analysis we observed that all types of erroneous rules tend to relate terms that are rather unlikely to co-occur together.


6 We also examined another filtering score, the cosine similarity between the vectors representing the two rule sides in LSA (Latent Semantic Analysis) space (Deerwester et al., 1990).

as the results with this filter resemble those for Dice we present results only for the simpler Dice filter.


We make two contributions in the paper: On one hand, we find an effective way of constructing high-quality semantic classes in the patternbased category which deals with multimembership.

we demonstrate, for the first time, that topic modeling can be utilized to help mining the peer relationship among words.


Wu's model can be understood as a strict hierarchical maximum-alignment method.

our alignments are soft (we sum over them), and we do not require strictly isomorphic syntactic structures.


It should be noted that exponential decay is not a good choice from a theoretical point of view, because it does not satisfy one of the necessary con-ditions for convergence-the sum of the learning rates must diverge to infinity (Spall, 2005).

this is probably not a big issue for practitioners because normally the training has to be terminated at a certain number of iterations in practice.


Finally, context may be of some help, but "test" is ambiguous between a noun and verb, and "gasolines" is only seen once in the training data, so there is no guarantee that context is sufficient to make a correct judgment.

some of the other contexts in which "reformulated" appears in the test set, such as "testing of reformulated gasolines," provide strong evidence that it can start a NP, since "of" is a highly reliable indicator that a NP is to follow.


will not encounter any previously unseen words.

to speed up training during our experiments and, in some cases, to avoid running out of memory, we replaced words appearing twice or fewer times in the data with the special symbol * UNKNOWN * .


Our unlabeled sample complexity results show that even with access to a small amount of unlabeled text, 6000 sentences more than what appears in the training and test sets, smoothing using the HMM yields 0.78 F1 on rare chunks.

the smoothed system requires 25,000 more sentences before it outperforms the baseline system on all chunks.


The entire process is fully automated and yields better performance than any existing state-of-the-art system, even though our models were not provided with any additional linguistic knowledge (for example, explicit syntactic constraints to avoid certain tag combinations such as "V V", etc.).

it is easy to model some of these linguistic constraints (both at the local and global levels) directly using integer programming, and this may result in further improvements and lead to new possibilities for future research.


Nakagawa (2004) described a training method based on a word-based Markov model and a character-based maximum entropy model that can be completed in a reasonable time.

this training method is limited by the generatively-trained Markov model in which informative features are hard to exploit.


Conditional random fields (CRFs) (Lafferty et al., 2001) further improve the performance (Kudo et al., 2004;Shi and Wang, 2007) by performing whole-sequence normalization to avoid label-bias and length-bias problems.

cRF-based algorithms typically require longer training times, and we observed an infeasible convergence time for our hybrid model.


Due to the obvious improvement brought by annotation adaptation to both word segmentation and Joint S&T, we can safely conclude that the knowledge can be effectively transferred from on an- Parsing F 1 % gold-standard segmentation 82.35 baseline segmentation 80.28 adapted segmentation 81.07  Table 4 we find that out of 30 word clusters appeared in the developing set of CTB, 13 clusters benefit from the annotation adaptation strategy, while 4 clusters suffer from it.

the compositive error rate of Recall for all word clusters is reduced by 20.66%, such a fact invalidates the effectivity of annotation adaptation.


proposed a method based on patterns of a sequence of morphemes (Monma et al., 2003).

the target of the research is closed-captions of Japanese TV shows, in which less than or equal to 2 lines text is displayed on a screen and the text all switches to other text at a time.


This ratio is less than half of that for all the bunsetsu boundaries.

when the bunsetsu boundary right after the bunsetsu which does not depend on the next bunsetsu, the ratio of linefeed insertion was 52.7%.


The general method is to let the two classifiers predict the class for a given sample, and if they agree, the hypothesized label is used.

when this agreement-based approach is used for prosodic event detection, we notice that there is not only difference in the labeling accuracy between positive and negative samples, but also an imbalance of the self-labeled positive and negative examples (details in Section 6).


Adding these samples has a negative impact on the classifier's performance.

our confidence-based approach balances the number of positive and negative samples and significantly reduces the error  rates for the negative samples as well, thus leading to performance improvement.


Specific instances of this general algorithm have recently been proposed for two linear similarity measures (Tromble et al., 2008;Zhang and Gildea, 2008).

the sentence similarity measures we want to optimize in MT are not linear functions, and so this fast algorithm for MBR does not apply.


Computing MBR even with simple non-linear measures such as BLEU, NIST or bagof-words F1 seems to require O(k 2 ) computation time.

these measures are all functions of features of e .


For example, taking a source parse as input, a tree-to-string decoder (e.g., ) pattern-matches the source parse with treeto-string rules and produces a string on the target side.

a string-to-tree decoder (e.g., (Galley et al., 2006;Shen et al., 2008)) is a parser that applies string-to-tree rules to obtain a target parse for the source string.


A common property of all the work mentioned above is that the combination models work on the basis of n-best translation lists (full hypotheses) of existing machine translation systems.

the n-best list only presents a very small portion of the entire search space of a Statistical Machine Translation (SMT) model while a majority of the space, within which there are many potentially good translations, is pruned away in decoding.


This may be regarded as favoring n-grams that are likely to appear in the reference translation (because they are likely in the derivation forest).

in order to score well on the BLEU metric for MT evaluation (Papineni et al., 2001), which gives partial credit, we would also like to favor lower-order ngrams that are likely to appear in the reference, even if this means picking some less-likely highorder n-grams.


Few types of arguments are shared between the chain and (fly X).

(charge X) shares many arguments with (accuse X), (search X) and (suspect X) (e.g., criminal and suspect).


Police pull over cars, but this schema does not have a chain involving cars.

(Y search) scores well with the 'police' chain and (search X) scores well in the 'defendant' chain too.


3 only works if the syntactic parse tree strictly follows the predicate-argument structure of the MR, since meaning composition at each node is assumed to combine a predicate with one of its arguments.

this assumption is not always satisfied, for example, in the case of verb gapping and flexible word order.


Previous literature in attribute extraction takes advantage of a range of data sources and extraction procedures (Chklovski and Gil, 2005;Tokunaga et al., 2005;Paşca and Van Durme, 2008;Yoshinaga and Torisawa, 2007;Probst et al., 2007;Wu and Weld, 2008).

these methods do not address the task of determining the level of specificity for each attribute.


Namely, the noun model performs significantly better (p < 0.05) than the adjective model, and the multiplicative model performs significantly better (p < 0.05) than the additive model.

the difference between the multiplicative model and the noun model is not statistically significant in this case.


That is, the noun model outperformed the adjective model and the multiplicative model outperformed the additive model when using attributespecifying adjectives.

for the objectmodifying adjectives, the noun model no longer outperformed the adjective model.


A zero-pronoun may or may not have its antecedent in the discourse; in the case it does, we say the zero-pronoun is anaphoric.

a zero-pronoun whose referent does not explicitly appear in the discourse is called a non-anaphoric zero-pronoun.


By comparing the dynamic cache model using correct zero-anaphora resolution (denoted by DCM (with ZAR) in Figure 4) and the one without it (DCM (w/o ZAR)), we can see that correct zeroanaphora resolution contributes to improving the caching for every cache size.

in the practical setting the current zero-anaphora resolu-tion system sometimes chooses the wrong candidate as an antecedent or does not choose any candidate due to wrong anaphoricity determination, negatively impacting the performance of the cache model.


For example, the 91.5 F-measure reported by McCallum and Wellner (2004) was produced by a system using perfect information for several linguistic subproblems.

the 71.3 F-measure reported by Yang et al.


Thus, we would expect a coreference resolution system to depend critically on its Named Entity (NE) extractor.

state-of-the-art NE taggers are already quite good, so improving this component may not provide much additional gain.


Any genrerelated differences in word usage and/or syntax have just meant a wider variety of words and sentences shaping the covereage of these tools.

ignoring this variety may actually hinder the development of robust language technology for analysing and/or generating multi-sentence text.


They achieve 81% accuracy in sense disambiguation on this corpus.

graphBank annotations do not differentiate between implicits and explicits, so it is difficult to verify success for implicit relations.


They found that even with millions of training examples, prediction results using all words were superior to those based on only pairs of non-function words.

since the learning curve is steeper when function words were removed, they hypothesize that using only non-function words will outperform using all words once enough training data is available.


For example, the pair rose:fell often indicates a Comparison relation when speaking about stocks.

occasionally authors refer to stock prices as "jumping" rather than "rising".


We show that the features in fact do not capture semantic relation but rather give information about function word co-occurrences.

they are still a useful source of information for discourse relation prediction.


These two conclusions are consistent with the original intuition.

using any single one does not provide competence in selecting the best set of features.


(2007) have investigated a model for jointly performing sentence-and document-level sentiment analysis, allowing the relationship between the two tasks to be captured and exploited.

the increased sophistication of supervised polarity classifiers has also resulted in their increased dependence on annotated data.


At first glance, it may seem plausible to apply an unsupervised clustering algorithm such as kmeans to cluster the reviews according to their polarity.

there is reason to believe that such a clustering approach is doomed to fail: in the absence of annotated data, an unsupervised learner is unable to identify which features are relevant for polarity classification.


In other words, the ability to determine the relevance of each feature is crucial to the accurate clustering of the ambiguous data points.

in the absence of labeled data, it is not easy to assess feature relevance.


Speaker attributes such as gender, age, dialect, native language and educational level may be (a) stated overtly in metadata, (b) derivable indirectly from metadata such as a speaker's phone number or userid, or (c) derivable from acoustic properties of the speaker, including pitch and f0 contours (Bocklet et al., 2008).

the goal of this paper is to model and classify such speaker attributes from only the latent information found in textual transcripts.


We reimplemented this model as our reference for gender classification, further details of which are given below: For each conversation side, a training example was created using unigram and bigram features with tf-idf weighting, as done in standard text classification approaches.

stopwords were retained in the feature set as various sociolinguistic studies have shown that use of some of the stopwords, for instance, pronouns and determiners, are correlated with age and gender.


The second observation is that, on the restricted subset of word pairs considered, the results obtained by word-to-word translation probabilities are most of the time better than those of concept vector measures.

the differences are not statistically significant.


Question Answering (QA), which aims to provide answers to human-generated questions automatically, is an important research area in natural language processing (NLP) and much progress has been made on this topic in previous years.

the objective of most state-of-the-art QA systems is to find answers to factual questions, such as "What is the longest river in the United States?"


Sentences with high scores are then added into the answer set or the summary.

to the best of our knowledge, all previous Markov Random Walk-based sentence ranking models only make use of topic relevance information, i.e.


While this limits the reliability of syntactic observations, it represents the current state of the art for syntactic analysis of unreconstructed spontaneous speech text.

automatically obtained parses for cleaned reconstructed text are more likely to be accurate given the simplified and more predictable structure of these SUs.


While OOV is always a problem for most languages in ASR, in the Chinese case the problem can be avoided by utilizing character n-grams and moderate performances can be obtained.

character ngram has its own limitation and proper addition of new words can increase the ASR performance.


Of course the paths going through a sub-edge e i should be definitely more than the paths through the corresponding full-edge e. As a result, P (e i |A) should usually be greater than P (e|A), as implied by the intuition.

the inter-connectivity between all sub-edges and the proper weights of them are not easy to be handled well.


Of course the modification of language models led by the addition and deletion of words is hard to quantify and we choose to add and delete as fewer words as possible, which is just a simple heuristic.

adding fewer words means that longer words are added.


for spoken document indexing fitted well with the proposed LAICA approach.


The fundamental principle of TBL is to employ a set of rules to correct the output of a stochastic model.

to traditional rule-based approaches where rules are manually developed, TBL rules are automatically learned from training data.


In particular, for onehour lectures given by different lecturers (such as, for example, invited presentations), it is often impractical to manually transcribe parts of the lecture that would be useful as training or development data.

transcripts for the first 10-15 minutes of a particular lecture can be easily obtained.


Hierarchical approaches to machine translation have proven increasingly successful in recent years (Chiang, 2005;Marcu et al., 2006;Shen et al., 2008), and often outperform phrase-based systems (Och and Ney, 2004;Koehn et al., 2003) on target-language fluency and adequacy.

their benefits generally come with high computational costs, particularly when chart parsing, such as CKY, is integrated with language models of high orders (Wu, 1996).


Regarding the small difference in BLEU scores on MT08, we would like to point out that tuning on MT05 and testing on MT08 had a rather adverse effect with respect to translation length: while the two systems are relatively close in terms of BLEU scores (24.83 and 24.91, respectively), the dependency LM provides a much bigger gain when evaluated with BLEU precision (27.73 vs. 28.79), i.e., by ignoring the brevity penalty.

the difference on MT08 is significant in terms of TER.


In other cases, the selected hypernyms were too generic words, such as entity or attribute, which also fail to preserve the sentence's meaning.

when the unknown term was a very specific word, hypernyms played an important role.


The results in Table 7 clearly show that the new model is beneficial.

we want to know how much of the improvement gained is due to the IS asymmetries, and how much the syntactic asymmetries on their own can contribute.


Human usually compress sentences by dropping the intermediate nodes in the dependency tree.

the resulting compressions retain both adequacy and fluency.


The tree trimming approach guarantees that the compressed sentence is grammatical if the source sentence does not trigger parsing error.

as we mentioned in Section 2, the tree trimming approach is not suitable for Japanese sentence compression because in many cases it cannot reproduce human-produced compressions.


Some researchers then tried to automatically extract paraphrase rules (Lin and Pantel, 2001; Barzilay and Lee, 2003;Zhao et al., 2008b), which facilitates the rule-based PG methods.

it has been shown that the coverage of the paraphrase patterns is not high enough, especially when the used paraphrase patterns are long or complicated (Quirk et al., 2004).


In the second stage, a NLG system is employed to generate a sentence t from r. s and t are paraphrases as they are both derived from r. The NLG-based methods simulate human paraphrasing behavior, i.e., understanding a sentence and presenting the meaning in another way.

deep analysis of sentences is a big challenge.


The basic idea is that a translation should be scored based on their similarity to the human references.


Accordingly, all paraphrases for the source units will be extracted as target units.

when a certain application is given, only the source and target units that can achieve the application will be kept.


More specifically, recall that LIBSVM trains a classifier that by default employs a CT of 0.5, thus classifying an instance as positive if and only if the probability that it belongs to the positive class is at least 0.5.

this may not be the optimal threshold to use as far as performance is concerned, especially for the minority classes, where the class distribution is skewed.


There exists some work to remove noise from SMS (Choudhury et al., 2007) (Byun et al., 2007) (Aw et al., 2006) (Kobus et al., 2008).

all of these techniques require aligned corpus of SMS and conventional language for training.


(2007) use queries as a source of knowledge for extracting prominent attributes for semantic concepts.

there has been much work on extracting structured information from larger text segments, such as addresses (Kushmerick 2001), bibliographic citations (McCallum et al.


Bilingual data (including bilingual sentences and bilingual terms) are critical resources for building many applications, such as machine translation (Brown, 1993) and cross language information retrieval (Nie et al., 1999).

most existing bilingual data sets are (i) not adequate for their intended uses, (ii) not up-to-date, (iii) apply only to limited domains.


Note that those bilingual names do not follow the parenthesis pattern.

most of them are identically formatted as: "{Number}。{English name}{Chinese name}{EndOfLine}".


Moreover, based on the assumption that anchor texts in different languages referring to the same web page are possibly translations of each other, (Lu et al., 2004) propose a novel approach to construct a multilingual lexicon by making use of web anchor texts and their linking structure.

since only famous web pages may have inner links from other pages in multiple languages, the number of translations that can be obtained with this method is limited.


According to the dictionary, "Little" can be linked with "小", and "River" can be linked with "河".

"Smoky" is translated as "冒烟 的" in the dictionary which does not match any Chinese characters in the Chinese snippet.


The matching process is actually quite simple, since we transform the learnt patterns into standard regular expressions and then make use of existing regular expression matching tools (e.g., Microsoft .Net Framework) to extract translation pairs.

to make our patterns more robust, when transforming the selected patterns into standard regular expressions, we allow each character class to match more than once.


For example, "大提琴与小提琴 双 重协 奏曲 Double Concerto for Violin and Cello D 大调第二交响曲 Symphony No.2 in D Major" is segmented into "大提琴与小提琴双重 协奏曲", "Double Concerto for Violin and Cello D", "大调第二交响曲", and "Symphony No.2 in D Major".

the ending letter "D" of the second segment should have been padded into the third segment.


The finding that longer dialogues were associated with higher user satisfaction disagrees with the results of many previous PARADISE-style evaluation studies.

it does confirm and extend the results of previous studies specifically addressing interactions between users and embodied agents: as in the previous studies, the users in this case seem to view the agent as a social entity with whom they enjoy having a conversation.


We hypothesize that the measures introduced in this section have larger power in differentiating different simulated user behaviors since every simulated user action contributes to the comparison between different simulations.

the measures introduced in Section 4.1 and Section 4.2 have less differentiating power since they compare at the corpus level.


Therefore, we suggest that the handcrafted user simulation is not sufficient to be used in evaluating dialog systems because it does not generate user actions that are as similar to human user actions.

the handcrafted user simulation is still better than a user simulation trained with not enough training data.


No significant difference is observed among the trained and the handcrafted simulations when comparing their generated corpora on corpus-level dialog features as well as when serving as the training corpora for learning dialog strategies.

the simulation trained from all available human user data can predict human user actions more accurately than the handcrafted simulations, which again perform better than the model trained from half of the human user corpus.


Therefore, our results suggest that if an expert is available for designing a user simulation when not enough user data is collected, it may be better to handcraft the user simulation than training the simulation from the small amount of human user data.

it is another open research question to answer how much data is enough for training a user simulation, which depends on many factors such as the complexity of the user simulation model.


Participants in dialogue assume that items they present will be added to the common ground unless there is evidence to the contrary.

participants do not always show acceptance of these items explicitly.


This group had an average WD score of .199, better than the rest of the group at .268.

skill does not appear to increase smoothly as more dialogues are completed.


In order to formalize this task as a sequential labeling problem, we have assumed that the label of a character is determined by the local information of the character and its previous label.

this assumption is not ideal for modeling abbreviations.


We will present a detailed discussion comparing DPLVM and CRF+GI for the English abbreviation generation task in the next subsection, where the difference is more significant.

to a larger extent, the results demonstrate that these two alternative approaches are complementary.


For A IT G and A BIT G , we can efficiently sum over the set of ITG derivations in O(n 6 ) time using the inside-outside algorithm.

for the ITG grammar presented in Section 2.2, each alignment has multiple grammar derivations.


Exhaustive computation of these quantities requires an O(n 6 ) dynamic program that is prohibitively slow even on small supervised training sets.

most of the search space can safely be pruned using posterior predictions from a simpler alignment models.


On the one hand this is expected because multiple occurrences of the same word does increase the confusion for word alignment and reduce the link confidence.

additional information (such as the distance of the word pair, the alignment of neighbor words) could indicate higher likelihood for the alignment link.


This is similar to the "loose phrases" described in (Ayan and Dorr, 2006a), which increased the number of correct phrase translations and improved the translation quality.

removing incorrect content word links produced cleaner phrase translation tables.


The networks in Figures 1b and 2b are examples.

as a CN is an integration of the skeleton and all hypotheses, it can be conceived as a list of the component translations.


The comparison between pairwise and incremental TER methods justifies the superiority of the incremental strategy.

the benefit of incremental TER over pair-wise TER is smaller than that mentioned in Rosti et al.


Note that RANK requires distinct D i , so a rank k RANK rule will first apply (optimally) as soon as the kth-best inside derivation item for a given edge is removed from the queue.

it will also still formally apply (suboptimally) for all derivation items dequeued after the kth.


Delayed Ranked Inside Derivation Deductions (Lazy Version of KA * ) using many derivations, each inside edge item will be popped exactly once during parsing, with a score and backpointers representing its 1-best derivation.

k-best lists involve suboptimal derivations.


The table also indicates that our method considerably outperformed two parsers on NP-COOD, ADJP-COOD, and UCP-COOD categories, but it did not work well on VP-COOD, S-COOD, and SBAR-COOD.

the parsers performed quite well in the latter categories.


As an example, in the case of discontinuous parsing discussed above, we have f = 2 for most practical cases.

lCFRS productions with a relatively large number of nonterminals are usually observed in real data.


A more efficient algorithm is presented in (Kuhlmann and , working in time O(|p|) in case of f = 2.

this algorithm works for a restricted typology of productions, and does not cover all cases in which some binarization is possible.


We conjecture that TT-MCTAG does not have such a closure property.

from a first inspection of the MC-TAG analyses proposed for natural languages (see Chen-Main and Joshi (2007) for an overview), it seems that there are no important natural language phenomena that can be described by LCFRS and not by TT-MCTAG.


This is because each derivation can have a distinct tcounter.

the definition of TT-MCTAG imposes that the head tree of each tuple contains at least one lexical element.


For example, when working in the financial domain we may be interested in the employment relation, but when moving to the terrorism domain we now may be interested in the ethnic and ideology affiliation relation, and thus have to create training data for the new relation type.

is the old training data really useless?


A number of relation extraction kernels have been proposed, including dependency tree kernels (Culotta and Sorensen, 2004), shortest dependency path kernels (Bunescu and Mooney, 2005) and more recently convolution tree kernels (Zhang et al., 2006;Qian et al., 2008).

in both feature-based and kernel-based studies, availability of sufficient labeled training data is always assumed.


Banko and Etzioni (2008) studied open domain relation extraction, for which they manually identified several common relation patterns.

our method obtains common patterns through statistical learning.


Our idea of concept pair clustering is a two-step clustering process: first it clusters concept pairs into clusters with good precision using dependency patterns; then it improves the coverage of the clusters using surface patterns.

the standard k-means algorithm is affected by the choice of seeds and the number of clusters k. as we claimed in the Introduction section, because we aim to extract relations from Wikipedia articles in an unsupervised manner, cluster number k is unknown and no good centroids can be predicted.


Since the clusters are obtained without any labeled data, they may not correspond directly to concepts that are useful for decision making in the problem domain.

the supervised learning algorithms can typically identify useful clusters and assign proper weights to them, effectively adapting the clusters to the domain.


Unreliable fields are highlighted so that the automatically annotated corpus can be corrected.

aL selection of examples together with partial manual labeling of the selected examples are the main foci of our work.


For SeSAL with t = 0.99, the delay  has no particularly beneficial effect.

in combination with lower thresholds, the delay rates show positive effects as SeSAL yields Fscores closer to the maximal F-score of 87.7 %, thus clearly outperforming undelayed SeSAL.


The most simple but guaranteed way would be to directly perform brute force search for the global optimum over the entire parameter space.

not only the computational cost of this so-called direct search would become undoubtfully expensive as the number of parameters increase, but most retrieval metrics are nonsmooth with respect to model parameters (Metzler, 2007).


For example, it is well known that the use of a phrase can be effective in retrieval when its constituent words appear very frequently in the collection, because each word would have a very low discriminative power for relevance.

if a constituent word occurs very rarely in the collection, it could not be effective to use the phrase even if the phrase is highly uncompositional.


In such cases, the knowledge-based model could be more useful, as it can find those query terms in the vocabulary.

the knowledge-based model would have a sparse vocabulary for languages that can have heavily inflected words such as Turkish, and Finnish.


For example, if the search engine's United States web site, which is considered as one of the most important markets in the world, was to employ such an approach, it'd only receive 74.9% accuracy by misclassifying the English queries entered from countries for which the default language is not English.

when this geographical information is used as a feature in our decision tree framework, we get a very high boost on the accuracy of the results for all the languages.


For example, in query pair (Toyota Camry, ), 9/13 English pages are anchored by the URLs containing keywords "toyota" and/or "camry", and 3/5 constraint documents' URLs also contain them.

the URLs of returned Chinese pages are less regular in general.


Interestingly, (political catoons) are among these Chinese queries improved most by English ranking, which is believed as rare (or sensitive) content on Chinese web.

top English queries are short of this type of queries.


Some connectives are largely unambiguous, such as although and additionally, which are almost always used as discourse connectives and the relations they signal are unambiguously identified as comparison and expansion, respectively.

not all words and phrases that can serve as discourse connectives have these desirable properties.


Similarly in sentence (2a), once is a discourse connective marking the temporal relation between the clauses "The asbestos fiber, crocidolite is unusually resilient" and "it enters the lungs".

in sentence (2b), once occurs with a non-discourse sense, meaning "formerly" and modifying "used".


instead of direct orthographic mapping, we model the mapping between an English segment and the pronunciation in Chinese.


For the Jlist, similarity between pronunciations accounted for nearly 80% of the errors, and the ratio for the errors that are related to compositions and pronunciations is 1:2.6.

for the Elist, the corresponding ratio is almost 1:1.


It should be easy to find a lexicon that contains pronunciation information about Chinese characters.

it might not be easy to find visually similar Chinese characters with computational methods.


With a lexicon, we can find characters that can be pronounced in a particular way.


It is easy to judge whether two Chinese characters have the same tone.

it is not trivial to define "similar" sound.


This enables  By using the adjoining operation, we avoid the problem of infinite local ambiguity.

the adjoining operation cannot preserve lexical dependencies of partial parse trees.


Under a regular CFG, each parse tree uniquely idenfifies a derivation.

multiple derivations in a TSG can produce the same parse; obtaining the parse probability requires a summation over all derivations that could have produced it.


Since the oracle score for CCGbank is less than 95%, it would not be a fair comparison to use the complete test set.

there are a number of sentences which are correct, or almost correct, according to EVALB after the conversion, and we are able to use those for a fair comparison.


We can retrieve positive examples from Web archive with high precision (but low recall) by manually augmenting queries with hypernyms or semantically related words (e.g., "Loft AND shop" or "Loft AND stationary").

it is often costly to create negative examples.


It mainly refers to a strip of red carpeting laid down for dignitaries to walk on.

it is possible to encounter instances of "red carpet" referring to any carpet of red colour.


Because the amount of the resources used in our study is quite different, we cannot directly compare the methods and results.

because our analyzer has scalability that can freely add new features, for our future work, we hope to adopt the case frames as new features and compare their effect.


The ASR accuracy also indicates the user's habituation.

it has been shown that the user's ASR accuracy and barge-in rate do not improve simultaneously (Komatani et al., 2007).


This algorithm starts by deleting all the clusters which are in QS from CS so that we only focus on the context clusters whose subtopics are present in the answers.

in some cases this assumption is incorrect 1 .


In this paper, we designed a set of new classes of features to generate better compressions, and they were found to produce statistically significant improvements over the state-of-the-art.

although the user study demonstrates the expected positive impact of grammatical features, an error analysis (Gupta et al., 2009) reveals some limitations to improvements that can be obtained using grammatical features that refer only to the source sentence structure, since the syntax of the source sentence is frequently not preserved in the gold standard compression.


Query-focus also aids the automated summarizers in directing the summary at specific topics, which may result in better agreement with these model summaries.

while query focus correlates with performance, we show that highperforming automatic systems produce summaries with disproportionally higher query term density than human summarizers do.


Initially, a rough estimation of the significance of a word is given by its frequency in the document set.

this simple frequency-based measure is obviously not accurate.


The rule table is reduced to the same size (22% of original table) using the two metrics, separately.

as shown in Table 2, the frequency method decreases the BLEU scores, while the C-value achieves improvements.


For the current synchronous grammars based SMT, to some extent, the generalization ability of the grammar rules (the usability of the rules for the new sentences) can be considered as a kind of the generative power of the grammar and the disam-biguition ability to the rule candidates can be considered as an embodiment of expressive power.

the generalization ability and the disambiguition ability often contradict each other in practice such that various grammar formalisms in SMT are actually different trade-off between them.


Through this formalization, we can see that FSCFG rules and LSTSSG rules are both included.

we should point out that the rules with mixture of X non-terminals and syntactic non-terminals are not included in our current implementation despite that they are legal under the proposed formalism.


Then, the duplicates will be discarded during scoring.

due to the high-degree generalization , the FSCFG abstract rules are more likely to be matched by the test sentences.


It takes into account inflexional variants and synonyms.

it is considerably more sophisticated and is highly dependent on the underlying large scale linguistic resources.


The correlation coefficient for the NIST score is still slightly negative, indicating that trying to take a word sequence's information content into account is hopeless at the sentence level.

the correlation coefficient for the BLEU score almost doubles from 0.078 to 0.133, which, however, is still unsatisfactory.


Our impression has always been that this is obviously true for standard commercial systems.

serious scientific publications (Somers, 2005;Koehn, 2005) come to the conclusion that back-translation is completely unsuitable for MT evaluation.


These obvious deficits have probably motivated reservations against such systems, and we agree that for such reasons they may be unsuitable for use at MT competitions.

1 there are numerous other applications where such considerations are of less import- 1 Although there might be a solution to this: It may not always be necessary that forward and backward translations are generated by the same MT system.


Tree-based statistical machine translation models have made significant progress in recent years, especially when replacing 1-best trees with packed forests.

as the parsing accuracy usually goes down dramatically with the increase of sentence length, translating long sentences often takes long time and only produces degenerate translations.


So splitting long sentences into sub- A simple way is to split long sentences by punctuations.

without concerning about the original whole tree structures, this approach will result in ill-formed sub-trees which don't respect to original structures.


Obviously, this is an unrealistic assumption in real translation.

we argue that tectogrammatical deep-syntactic dependency trees (as introduced in the Functional Generative Description framework, (Sgall, 1967)) are relatively close to this requirement, which makes the HMTM approach practically testable.


After determining the radius, we replace each word with its POS tag; in order to reflect various expressions of each sentence, POS tags are more proper than lexical information of actual words.

since CKs play the most important role to discriminate comparative sentences, they are represented as a combination of their actual keyword and POS tag.


It is a collection of blog entries in English, Spanish and Italian.

for this research we used the first two languages.


The basic idea of the RelF score is to give those words a high score, which occur frequently in the context of a weasel tag.

due to the sparseness of tagged instances, words that occur with a very high frequency in the corpus automatically receive a lower score than low-frequent words.


A large subset of these searches is subjective in nature, where the user is looking for different images for a single concept (Linsley, 2009).

it is a common user experience that the images returned are not relevant to the intended concept.


The user can query one or more translations to get the relevant images.

this method puts the onus of choosing a translation on the user.


Most large software projects have bug tracking systems, e.g., Bugzilla 1 , to help global users to describe and report the bugs they encounter when using the software.

since the same bug may be seen by many users, many duplicate bug reports are sent to bug tracking systems.


Hence, bug reports interest us because (1) they are abundant and freely available,(2) they naturally form a semiparallel corpus, and (3) they contain many technical terms.

bug reports have characteristics that raise many new challenges.


At these sites, travel blogs are manually registered by bloggers themselves, and the blogs are classified by their destinations.

there are many more travel blogs in the blogos-phere.


Assigning a score to the players deals with the coreferential pairs.

motivated by (Passonneau, 2004) and others, the evaluation handles the coreferential pairs in a way demonstrated in Figure 2.


TV_ConSem (Ji and Lu, 2007) calculates the percentage of context words in a domain lexicon using both frequency information and semantic information.

this technique requires a domain lexicon whose size and quality have great impact on the performance of the algorithm.


This arrangement allows more annotators to participate, and reduces logistical problems.

having no full-time annotators limits the overall weekly annotation rate.


Annotator Speeds Our POS and syntax annotation rate is 540 tokens/hour (with some reaching rates as high as 715 tokens/hour).

due to the current part-time arrangement, annotators worked an average of only 6 hours/week, which meant that data was annotated at an average rate of 15K tokens/week.


Since many-to-many links are commonly observed in natural language, symmetrization is able to make up for this modeling limitation.

combining two directional alignments practically can lead to improved performance.


Previous studies have attempted to resolve this problem by removing unnecessary function words, or by reordering source sentences.

the removal of function words can cause a serious loss in information.


The basic scheme to prune a translation table is to delete all translation pairs that have significance values smaller than a given threshold.

in practice, this pruning scheme does not work well with phrase tables, as many phrase pairs receive the same significance values.


This is basically achieved by using dynamic programming and especially the Viterbi algorithm associated with beam searching.

decoding algorithms were designed for translation, not for paraphrase generation.


ℓ 1 penalty methods such as Lasso, being convex, can be solved by optimization and give guaranteed optimal solutions.

ℓ 0 penalty methods, like stepwise feature selection, give approximate solutions but produce models that are much sparser than the models given by ℓ 1 methods, which is quite crucial in WSD (Florian and Yarowsky, 2002).


There is a high concentration of chain starts and ends near the boundary which leads to a misclassification if we only combine chain starts and ends for segmentation.

there are also a large number of chain continuations across the utterance boundary, which implies that a story boundary is less likely.


Acquisition of prosody, in addition to vocabulary and grammar, is essential for language learners.

intonation has been less-emphasized both in classroom and computer-assisted language instruction (Chun, 1998).


linear-chain), an exact inference can be obtained efficiently if the number of output labels is not large.

for large number of output labels, the inference is often prohibitively expensive.


In most results, Method 1 was the fastest, because it was terminated after fewer iterations.

method 1 sometimes failed to converge, for example, in Encyclopedia.


Since S w+ztet (x, y) = S w (x, y) if f t (x, y) = 0, this procedure reduces the the O(#nz) operations to O(#nz/n) = O(l).

it needs extra spaces to store all S w (x, y) and T w (x).


In this context, Tree Edit Distance (TED) has been widely used for many years.

one of the main constraints of this method is to tune the cost of edit operations, which makes it difficult or sometimes very challenging in dealing with complex problems.


Most of the existing multi-document summarization methods decompose the documents into sentences and work directly in the sentence space using a term-sentence matrix.

the knowledge on the document side, i.e.


Indeed, the first GIVE Challenge acquired data from over 1100 experimental subjects online.

it still remains to be shown that the results that can be obtained in this way are in fact comparable to more established task-based evaluation efforts, which are based on a carefully selected subject pool and carried out in a controlled laboratory environment.


Among this group of subjects, 93% self-rated their English proficiency as "expert" or better; 81% were native speakers.

to the online experiment, 31% of participants were male and 65% were female (4% did not specify their gender).


This could be taken as a possible deficit of the Internet-based evaluation.


Our approach is similar to Kibble and Power (2004) in that we don't use the concept of centering transitions.

our method is more efficient in that Kibble and Power (2004) use centering transitions to rank the set of generated solutions (some of which are incoherent), whereas we encode centering constraints in elementary trees to reduce the search space of possible solutions before we start computing them.


(2006) previously explored the use of statistical learningbased classifiers trained with lexical features, such as character and word n-grams, for mobile spam filtering.

content-based spam filtering directed at SMS messages are very challenging, due to the fact that such messages consist of only a few words.


We may speculate that the difference is primarily due to our limited training set size (1,680 questions versus 21,500 questions for Li & Roth).

we are not aware of any work attempting to extract AP on word level using machine learning in order to provide dynamic classes to a question classification module.


(2006a) (henceforth,GGJ06) to develop an approximation using expected counts.

we show here that their approximation is flawed in two respects: 1) It omits an important factor in the expectation, and 2) Even after correction, the approximation is poor for hierarchical models, which are commonly used for NLP applications.


Raj and Whittaker (2003) showed a general Ngram language model structure and introduced a lossless algorithm that compressed a sorted integer vector by recursively shifting a certain number of bits and by emitting index-value inverted vectors.


Thus, if w 1 w 2 w 3 w 4 w 5 was not observed in the training corpus, p(w In most smoothing methods, the lower-order models, for all N > 1, are recursively estimated in the same way as the highest-order model.

the smoothing method of Kneser and Ney (1995) and its variants are the most effective methods known (Chen and Goodman, 1998), and they use a different way of computing N-gram counts for all the lower-order models used for smoothing.


Figure 2: F-measure over time for test set 98b with configurations: As can be seen, there is a small gain in performance by using seeds within the epoch of the test set, but the decay is still observable as we increase the time gap between the unlabeled data and the test set.

if we use unlabeled data within the epoch of the test set, we hardly see a degradation trend as the time gap between the epochs of seeds and test set is increased.


Union is performed between the baseline system's list and the lists produced by the other systems to create lists of pairs that include the information in the baseline.

each of the following systems' outputs are merged separately with the baseline.


DTs have a natural ordering of the children of the nodes induced by the position of the corresponding words in the sentence.

pTs introduce new intermediate nodes to better express the syntactical structures of a sentence in terms of phrases.


Most of the prior work focused on the sentence level by clustering sentences into topics and ordering sentences on a time line.

many sentences in news articles include multiple events with different time arguments.


The 2005 ACE evaluation had 8 types of events, with 33 subtypes; for the purpose of this paper, we will treat these simply as 33 distinct event types.

to ACE event extraction, we exclude generic, negative, and hypothetical events.


The time argument "Saturday" was mistakenly propagated from the "Conflict-Attack" event "battles" to "shot" because they share the same Time-cue role "instrument" ("small arms/gun").

the correct time argument for the "shot" event should be "Monday" as indicated in the "gunfire/explosions" event in the previous context sentence.


Our new parsing algorithms could be implemented by defining the "sense" of each word as the index of its head.

when parsing with senses, the complexity of the Eisner (2000) parser increases by factors of O(S 3 ) time and O(S 2 ) space (ibid., Section 4.2).


The most apparent similarity between our model and the transition-based category is that they all need a classifier to perform classification conditioned on a certain configuration.

they differ from each other in the classification results.


with:1:0-NULL:2:1-fork:3:1 ate:1:0-the:2:3-meat:3:1 ate:1:0-with:2:1-fork:3:2 with:1:0-a:2:3-fork:3:1 NULL:1:2-He:2:3-ate:3:0 He:1:3-NULL:2:1-ate:3:0 ate:1:0-meat:2:1-NULL:3:2 ate:1:0-NULL:2:3-with:3:1 with:1:0-fork:2:1-NULL:3:2 NULL:1:2-a:2:3-fork:3:0 a:1:3-NULL:2:1-fork:3:0 ate:1:0-NULL:2:3-.:3:1 ate:1:0-.:2:1-NULL:3:2 (b) NULL:1:2-the:2:3-meat:3:0 the:1:3-NULL:2:1-meat:3:0  To provide bilingual subtree constraints, we need to find the characteristics of subtree mapping for the two given languages.


translation Both Chinese and English are classified as SVO languages because verbs precede objects in simple sentences.

chinese has many characteristics of such SOV languages as Japanese.


The problem of appropriately selecting one of them to work with would ideally be solved by statistical methods (Higgins and Sadock, 2003) or knowledge-based inferences.

no such approach has been worked out in sufficient detail to support the disambiguation of treebank sentences.


This is based on the observation that the readings of a semantically ambiguous sentence are partially ordered with respect to logical entailment, and the weakest readings -the minimal (least informative) readings with respect to this order -only express "safe" information that is common to all other read-ings as well.

when a sentence has millions of readings, finding the weakest reading is a hard problem.


In our example, (b) and (c) are indeed normal forms with respect to a rewrite system that consists only of the rule (1).


(2008), in that we perform redundancy elimination by intersecting tree grammars.

the construction we present here is much more general: The algorithmic foundation for redundancy elimination is now exactly the same as that for weakest readings, we only have to use an equivalencepreserving rewrite system instead of a weakening one.


We could then perform such inferences on (cleaner) semantic representations, rather than strings (as they do).

it may be possible to reduce the set of readings even further.


They report an accuracy of 81.6% and 84.5% for different classification schemes.

apart from a plural feature, all heuristics are tailored to specific properties of the Wikipedia resource.


Default reasoning (Reiter, 1980) is confronted with severe efficiency problems and therefore has not extended beyond experimental systems.

the emerging paradigm of Answer Set Programming (ASP, Lifschitz (2008)) seems to be able to model exceptions efficiently.


We also find annotated examples of generic NPs that are not discussed in the formal semantics literature (8.a), but that are well captured by the ACE-2 guidelines.

there are also cases that are questionable (8.b).


However, such methods require expensive resources such as domain experts to hand-code the rules, or a corpus of expertlayperson interactions to train on.

we present a corpus-driven framework using which a user-adaptive REG policy can be learned using RL from a small corpus of non-adaptive humanmachine interaction.


Such systems learned from the user at the start and later adapted to the domain knowledge of the users.

they either require expensive expert knowledge resources to hand-code the inference rules (Cawsey, 1993) or large corpus of expert-layperson interaction from which adaptive strategies can be learned and modelled, using methods such as Bayesian networks (Akiba and Tanaka, 1994).


The user may have the capacity to learn jargon.

only the user's initial knowledge is recorded.


Several user simulation models have been proposed for use in reinforcement learning of dialogue policies (Georgila et al., 2005;Schatzmann et al., 2006;Schatzmann et al., 2007;Ai and Litman, 2007).

they are suited only for learning dialogue management policies, and not natural language generation policies.


Only the entity that it is referring to (R i ) and its type (T i ) are used.

the above model simulates the process of interpreting and resolving the expression and identifying the domain entity of interest in the instruction.


In most studies done previously, the user's domain knowledge is considered to be static.

in real conversation, we found that the users nearly always learned jargon expressions from the system's utterances and clarifications.


This is because learned policies use a few jargon expressions (giving rise to clarification requests) to learn about the user.

the Jargon policy produces more user learning gain because of the use of more jargon expressions.


Automated summarization systems which enable user to quickly digest the important information conveyed by either a single or a cluster of documents are indispensible for managing the rapidly growing amount of textual information and multimedia content (Mani and Maybury, 1999).

due to the maturity of text summarization, the research paradigm has been extended to speech summarization over the years (Furui et al., 2004;McKeown et al., 2005).


Then, the cosine similarity between any given two sen- The loss function is thus defined by Once the sentence generative model   have been properly estimated, the summary sentences can be selected iteratively by (8) according to a predefined target summarization ratio.

as can be seen from (8), a new summary sentence is selected without considering the redundant information that is also contained in the already selected summary sentences.


One possible explanation is that the structural evidence of the spoken documents in the test set is not strong enough for CRF to show its advantage of modeling the local structural information among sentences.

lexRank gives a very promising performance in spite that it only utilizes lexical information in an unsupervised manner.


There has been a tremendous upsurge of interest in documentary linguistics, the field concerned with the the "creation, annotation, preservation, and dissemination of transparent records of a language" (Woodbury, 2010).

documentary linguistics alone is not equal to the task.


The product is a Universal Corpus, 1 in two senses of universal: in the sense of including (ultimately) all the world's languages, and in the sense of enabling software and processing methods that are language-universal.

we do not aim for a collection that is universal in the sense of encompassing all language documentation efforts.


The fact that these layers will exist in diminishing quantity is also unavoidable.

there is an important consequence: the primary texts will be permanently subject to new translation initiatives, which themselves will be subject to new alignment and glossing initiatives, in which each step is an instance of semisupervised learning (Abney, 2007).


To be successful, the Human Language Project would require substantial funds, possibly drawing on a constellation of public and private agencies in many countries.

in the spirit of starting small, and starting now, agencies could require that sponsored projects which collect texts and build lexicons contribute them to the Language Commons.


Today, language documentation is a high priority in mainstream linguistics.

the field of computa-tional linguistics is yet to participate substantially.


Methods based on word strings (e.g., BLEU (Papineni et al., 2002), NIST(NIST, 2002), METEOR (Banerjee and Lavie., 2005), ROUGE-L (Lin and Och, 2004), and IMPACT(Echizen-ya and Araki, 2007)) calculate matching scores using only common words between MT outputs and references from bilingual humans.

these methods cannot determine the correct word correspondences sufficiently because they fail to focus solely on phrase correspondences.


WOE parse has a dramatic performance improvement over TextRunner.

the improvement comes at the cost of speed -TextRunner  Figure 2: WOE pos performs better than TextRunner, especially on precision.


Their results showed limited advantage of parser features over shallow features for IE.

our results imply that abstracted dependency path features are highly informative for open IE.


Several parallel efforts have been made recently to improve the efficiency of IE tasks by optimizing low-level feature extraction (Ramakrishnan et al., 2006;Chandel et al., 2006) or by reordering operations at a macroscopic level (Ipeirotis et al., 2006;Shen et al., 2007;Jain et al., 2009).

to the best of our knowledge, SystemT is the only IE system in which the optimizer generates a full end-to-end plan, beginning with low-level extraction primitives and ending with the final output tuples.


We then construct a network where characters are vertices and edges signify an amount of bilateral conversation between those characters, with edge weights corresponding to the frequency and length of their exchanges.

to previous approaches to social network construction, ours relies on a novel combination of patternbased detection, statistical methods, and adaptation of standard natural language tools for the literary genre.